Characterization of Enzymatically Interesterified Canola Oil and Fully‐Hydrogenated Canola Oil Blends Under Supercritical CO<sub>2</sub>
Bibliographic record
Abstract
Abstract Blends of canola oil and fully‐hydrogenated canola oil (FHCO) containing 10, 30, and 50 wt% FHCO were interesterified enzymatically using Lipozyme TL IM (6 % of initial substrates, w/v) under supercritical CO2 at 10 MPa and 65 °C for 2 h. Changes in polymorphic behavior and crystal morphology of non‐interesterified initial blends (NIB) and purified enzymatically interesterified products (PEIP) were studied using X‐ray diffraction spectroscopy (XRD) and polarized light microscopy. As well, the effects of blend ratio and enzymatic interesterification on rheological behavior were investigated. XRD analysis demonstrated the predominance of α form in FHCO while blending it with canola oil induced the formation of β form after crystallizing the samples at 24 and 5 °C for 12 h. Enzymatic interesterification caused the appearance of β′ forms and dramatically changed crystal morphology. The PEIP samples contained fewer crystal particles compared to NIB, but the crystals were more symmetrical. The elastic modulus (solid‐like behavior) (G′) was lower in NIB with 30 wt% FHCO compared to the one with 50 wt% FHCO. Enzymatic interesterification also had a strong effect on G′ of the samples as it decreased after interesterification. The results of this study will help the development of conversion technologies under supercritical conditions in order to formulate more healthy fats having appropriate functional properties to address the industrial demand for the production of margarine and pastry shortenings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".